Machine Learning and Deep Learning Integration for Skin Diseases Prediction

Machine Learning and Deep Learning Integration for Skin Diseases Prediction

© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Samir Kumar Bandyopadhyay, Payal Bose, Amiya Bhaumik, Sandeep Poddar
DOI :  10.14445/22315381/IJETT-V70I2P202

How to Cite?

Samir Kumar Bandyopadhyay, Payal Bose, Amiya Bhaumik, Sandeep Poddar, "Machine Learning and Deep Learning Integration for Skin Diseases Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 13-21, 2022. Crossref,

Living creature skin disease is a fairly prevalent ailment. In the medical world, monitoring dermatological disorders and classifying them is a complex process. Due to the sheer intricacy of individual skin tone and the visible proximity effect of infections, recognizing the precise type can be challenging at times. As a result, it is critical to diagnose and recognize skin disease as soon as possible. Artificial intelligence (AI) is quickly expanding in therapeutic areas in a modern context. For diagnostic purposes, much deep learning (DL) and machine learning (ML) methods are applied. These strategies drastically enhance the diagnosing process while also speeding it up. In this study, to improve disease detection, a model combining deep learning (DL) and machine learning (ML) has been developed. For classification, three sets of machine learning models were utilized, and for feature selection, four sets of pre-trained deep learning models were being used. For classification models, deep neural networks Alexnet, Googlenet, Resnet50, and VGG16 were used, while Support Vector Machine, Decision tree, and Ensemble boosting Adaboost classifier were applied for classification. To identify the best prediction model, a comparative study was carried out. The hybrid method Resnet50 with SVM produced the best results, with 99.11% accuracy.

Deep Feature Extraction, Deep Learning (DL), Machine Learning (ML), Skin Diseases Detection, Support Vector Machine (SVM).

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